Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-3003-2025
https://doi.org/10.5194/gmd-18-3003-2025
Development and technical paper
 | 
26 May 2025
Development and technical paper |  | 26 May 2025

A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature

Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1886', Anonymous Referee #1, 30 Aug 2024
    • AC1: 'Reply on RC1', Conor Doherty, 06 Oct 2024
  • RC2: 'Comment on egusphere-2024-1886', Carles Milà, 18 Sep 2024
    • AC2: 'Reply on RC2', Conor Doherty, 06 Oct 2024
  • RC3: 'Comment on egusphere-2024-1886', Nils-Otto Kitterød, 20 Sep 2024
    • AC3: 'Reply on RC3', Conor Doherty, 06 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Conor Doherty on behalf of the Authors (06 Jan 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Jan 2025) by Rohitash Chandra
RR by Carles Milà (17 Jan 2025)
RR by Anonymous Referee #3 (19 Jan 2025)
ED: Publish as is (29 Jan 2025) by Rohitash Chandra
AR by Conor Doherty on behalf of the Authors (03 Feb 2025)  Manuscript 
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Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
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